RNA sequencing reveals a transcriptomic portrait of human mesenchymal stem cells from bone marrow, adipose tissue, and palatine tonsils.
Kyung-Ah ChoMinhwa ParkYu-Hee KimSo-Youn WooKyung-Ha RyuPublished in: Scientific reports (2017)
Human mesenchymal stem cells (MSCs) are adult multipotent cells that have plasticity and inhabit the stroma of diverse tissues. The potential utility of MSCs has been heavily investigated in the fields of regenerative medicine and cell therapy. However, MSCs represent diverse populations that may depend on the tissue of origin. Thus, the ability to identify specific MSC populations has remained difficult. Using RNA sequencing, we analyzed the whole transcriptomes of bone marrow-derived MSCs (BMs), adipose tissue-derived MSCs (AMs), and tonsil-derived MSCs (TMs). We categorized highly regulated genes from these MSC groups according to functional gene ontology (GO) classification. AMs and TMs showed higher expression of genes encoding proteins that function in protein binding, growth factor, or cytokine activity in extracellular compartments than BMs. Interestingly, TM were highly enriched for genes coding extracellular, protein-binding proteins compared with AMs. Functional Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis also showed differentially enriched signaling pathways between the three MSC groups. Further, we confirmed surface antigens expressed in common and in a tissue-specific manner on BMs, AMs, and TMs by flow cytometry analysis. This study provides comprehensive characteristics of MSCs derived from different tissues to better understand their cellular and molecular biology.
Keyphrases
- mesenchymal stem cells
- cell therapy
- umbilical cord
- bone marrow
- adipose tissue
- genome wide
- growth factor
- single cell
- genome wide identification
- endothelial cells
- flow cytometry
- bioinformatics analysis
- induced apoptosis
- binding protein
- gene expression
- insulin resistance
- stem cells
- signaling pathway
- genome wide analysis
- transcription factor
- machine learning
- deep learning
- dna methylation
- epithelial mesenchymal transition
- high fat diet
- type diabetes
- metabolic syndrome
- young adults
- cell death
- endoplasmic reticulum stress
- oxidative stress
- single molecule
- long non coding rna
- risk assessment
- human health